Catherine Boden | PubMed Health

PubMed2002

The structure-function relationship in eyes with glaucomatous visual field loss that crosses the horizontal meridian.

OBJECTIVE: To evaluate the relationship between visual field loss and glaucomatous optic discs in eyes in which field loss spreads across the horizontal meridian. SUBJECTS AND METHODS: Ninety-six patients with glaucoma (9 advanced, 60 moderate, and 27 early) with 2 successive abnormal fields were included. Standard achromatic automated perimetry defects were identified with a nerve fiber bundle map to identify abnormal sectors. Crossover was present if the superior and inferior sectors at the horizontal meridian (nasal, central, or temporal) were both abnormal. Optic disc damage was assessed by masked grading of simultaneous stereophotographs. RESULTS: Only 30% (29) of glaucomatous eyes showed crossover, and only 2 of those eyes had early loss. The most frequent pattern of visual field loss (41% of eyes) was single hemifield damage with defects in contiguous sectors. Regardless of the pattern or severity of visual loss, most eyes (66 [69%] of 96) had both superior and inferior optic disc damage. CONCLUSIONS: Early glaucomatous visual field loss rarely crosses the horizontal meridian, but defects in both hemifields at the horizontal meridian are more common in more advanced field loss. In 26 (90%) of 29 eyes with crossover, it could be explained by changes at the optic nerve head.

Adult↗

PubMed2002

Using machine learning classifiers to identify glaucomatous change earlier in standard visual fields.

PURPOSE: To compare the ability of several machine learning classifiers to predict development of abnormal fields at follow-up in ocular hypertensive (OHT) eyes that had normal visual fields in baseline examination. METHODS: The visual fields of 114 eyes of 114 patients with OHT with four or more visual field tests with standard automated perimetry over three or more years and for whom stereophotographs were available were assessed. The mean (+/-SD) number of visual field tests was 7.89 +/- 3.04. The mean number of years covered (+/-SD) was 5.92 +/- 2.34 (range, 2.81-11.77). Fields were classified as normal or abnormal based on Statpac-like methods (Humphrey Instruments, Dublin, CA) and by several machine learning classifiers. The machine learning classifiers were two types of support vector machine (SVM), a mixture of Gaussian (MoG) classifier, a constrained MoG, and a mixture of generalized Gaussian (MGG). Specificity was set to 96% for all classifiers, using data from 94 normal eyes evaluated longitudinally. Specificity cutoffs required confirmation of abnormality. RESULTS: Thirty-two percent (36/114) of the eyes converted to abnormal fields during follow-up based on the Statpac-like methods. All 36 were identified by at least one machine classifier. In nearly all cases, the machine learning classifiers predicted the confirmed abnormality, on average, 3.92 +/- 0.55 years earlier than traditional Statpac-like methods. CONCLUSIONS: Machine learning classifiers can learn complex patterns and trends in data and adapt to create a decision surface without the constraints imposed by statistical classifiers. This adaptation allowed the machine learning classifiers to identify abnormality in visual field converts much earlier than the traditional methods.

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